Contents

The classical
view

Classical categorization comes to us first from
Plato, who, in his Statesman dialogue, introduces the
approach of grouping objects based in their similar properties. This approach was
further explored and systematized by Aristotle in his Categories treatise, where he
analyzes the differences between classes and objects. Aristotle also applied
intensively the classical categorization scheme in his approach to
the classification of living beings (which uses the technique of
applying successive narrowing questions such as "Is it an animal or
vegetable?", "How many feet does it have?", "Does it have fur or
feathers?", "Can it fly?"...), establishing this way the basis for
natural taxonomy.

According to the classical view, categories should be clearly
defined, mutually exclusive and collectively exhaustive. This way,
any entity of the given classification universe belongs
unequivocally to one, and only one, of the proposed categories.

Conceptual
clustering

Conceptual clustering is a modern variation of
the classical approach, and derives from attempts to explain how knowledge is represented.
In this approach, classes (clusters or entities) are generated by first
formulating their conceptual descriptions and then classifying the
entities according to the descriptions.

Conceptual clustering developed mainly during the 1980s, as a
machine paradigm for unsupervised learning. It is
distinguished from ordinary data clustering by generating a
concept description for each generated category.

Categorization tasks in which category labels are provided to
the learner for certain objects are referred to as supervised
classification, supervised learning, or concept
learning. Categorization tasks in which no labels are supplied
are referred to as unsupervised classification, unsupervised learning, or data
clustering. The task of supervised classification involves
extracting information from the labeled examples that allows
accurate prediction of class labels of future examples. This may
involve the abstraction of a rule or concept relating observed object features to
category labels, or it may not involve abstraction (e.g., exemplar
models). The task of clustering involves recognizing inherent
structure in a data set and grouping objects together by similarity into classes.
It is thus a process of generating a classification
structure.

Conceptual clustering is closely related to fuzzy set theory, in which objects may belong
to one or more groups, in varying degrees of fitness.

Prototype
Theory

Since the research by Eleanor Rosch and George Lakoff in
the 1970s, categorization can also be viewed as the process of
grouping things based on prototypes - the idea of necessary and
sufficient conditions is almost never met in categories of
naturally occurring things. It has also been suggested that
categorization based on prototypes is the basis for human
development, and that this learning relies on learning
about the world via embodiment.

A cognitive approach
accepts that natural categories are graded (they tend to be fuzzy
at their boundaries) and inconsistent in the status of their
constituent members.

Systems of categories are not objectively "out there" in the
world but are rooted in people's experience. Conceptual categories
are not identical for different cultures, or indeed, for every
individual in the same culture.

Categories form part of a hierarchical structure when applied to
such subjects as taxonomy
in biological classification:
higher level: life-form level, middle level: generic or genus level, and lower level: the
species level. These can be
distinguished by certain traits that put an item in its distinctive
category. But even these can be arbitrary and are subject to
revision.

Categories at the middle level are perceptually and conceptually
the more salient. The generic level of a category tends to elicit
the most responses and richest images and seems to be the
psychologically basic level. Typical taxonomies in zoology for
example exhibit categorization at the embodied level, with similarities
leading to formulation of "higher" categories, and differences
leading to differentiation within categories.

Miscategorization

Miscategorization can be a logical fallacy in which diverse and dissimilar
objects, concepts, entities, etc. are grouped together based upon
illogical common denominators, or common denominators that
virtually any concept, object or entity have in common. A common
way miscategorization occurs is through an over-categorization of
concepts, objects or entities, and then miscategorization based
upon overly-simliar variables that virtually all things have in
common.